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Found 8 papers, 7 papers with code
Dissecting vocabulary biases datasets through statistical testing and automated data augmentation for artifact mitigation in Natural Language Inference
In this work, we focus on investigating dataset artifacts and developing strategies to address these issues.
The Bjøntegaard Bible -- Why your Way of Comparing Video Codecs May Be Wrong
Using additional supporting points inbetween standard points defined by parameters such as the quantization parameter, we assess the interpolation error of the Bj{\o}ntegaard-Delta (BD) calculus and its impact on the final BD value.
Deep probabilistic model for lossless scalable point cloud attribute compression
In recent years, several point cloud geometry compression methods that utilize advanced deep learning techniques have been proposed, but there are limited works on attribute compression, especially lossless compression.
Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model
In recent years, we have witnessed the presence of point cloud data in many aspects of our life, from immersive media, autonomous driving to healthcare, although at the cost of a tremendous amount of data.
Learning-based Lossless Point Cloud Geometry Coding using Sparse Tensors
Most point cloud compression methods operate in the voxel or octree domain which is not the original representation of point clouds.
Lossless Coding of Point Cloud Geometry using a Deep Generative Model
This paper proposes a lossless point cloud (PC) geometry compression method that uses neural networks to estimate the probability distribution of voxel occupancy.
Multiscale deep context modeling for lossless point cloud geometry compression
We propose a practical deep generative approach for lossless point cloud geometry compression, called MSVoxelDNN, and show that it significantly reduces the rate compared to the MPEG G-PCC codec.
Learning-based lossless compression of 3D point cloud geometry
On the one hand, octree representation can eliminate the sparsity in the point cloud.
